Long‐term ecological data confirm and refine conservation assessment of critically endangered swift parrots

• Published in: Animal conservation 2023-08, Vol.26 (4), p.450-463
• Main Authors: Owens, G., Heinsohn, R., Crates, R., Stojanovic, D.
• Format: Article
• Language: English
• Subjects: Assessments; Conservation; conservation assessment; Data collection; Discoloration; Endangered & extinct species; Endangered animals; Endangered species; forest landscape integrity; History; introduced predator; Lathamus discolor; Life history; Mathematical models; nest survival; Nests; Parameter estimation; Parameter identification; Parameter uncertainty; Parrots; Population decline; Population viability; population viability analysis; Prediction models; Rare species; Survival; swift parrot; Threatened species; Trends; Uncertainty; Wildlife conservation
• ISSN: 1367-9430; 1469-1795
• DOI: 10.1111/acv.12834

Conservation assessments of threatened species are often limited by scarce data and parameter uncertainty. Predictive models, designed to incorporate this uncertainty, may be the only tool available to inform conservation assessments for data‐deficient species, but they are used surprisingly rarely for this purpose. The swift parrot Lathamus discolor is the only critically endangered bird to be listed in Australia based on population viability analysis (PVA). We aimed to evaluate the accuracy of the 2015 conservation assessment, which used sparse information, by incorporating new detailed and long‐term data. First, we updated a range of life history parameter estimates, and then we repeated the same PVA as per the original conservation assessment. This process confirmed our earlier finding that swift parrot nests were more likely to survive in places with high mature forest cover. We identify that high forest landscape integrity and abundant hollow‐bearing trees best predict nest daily survival rates. Based on the updated PVA, we predict a 92.3% population decline over three generations (11 years). This supported the predictions of the original conservation assessment, and the main benefit of the additional data was improved confidence in projections (the magnitude and direction of the population decline were similar between the original and updated PVAs). Our results demonstrate that meaningful trends can be inferred for species with imperfect information about their life history. Using predictive models like PVAs can help managers identify which life history parameters impact most on demographic trends. This information can guide targeted data collection so that ‘draft’ models can be later updated to improve certainty around population predictions. Demographic models may form the basis of conservation assessments for species with limited data but are relatively underused tools. We evaluate a conservation assessment for a critically endangered bird by repeating a population viability analysis (PVA) with more than twice the data. Our updated PVA results support the predictions of the accurate but less precise original assessment. We highlight that decline is faster than previously estimated (92.3% over 11 years) and that nest survival is predicted by forest landscape integrity and hollow‐bearing trees. We conclude that PVAs based on sparse or imperfect data can predict meaningful trends for threatened species.